Bibliographic record
Abstract
The Canadian Water Network sponsored a 2-year knowledge translation project to review innovative stormwater management initiatives that are taking place across Canada. Three workshops were held in Vancouver, Calgary and Toronto that featured innovative stormwater practices in the different cities. The results showed that there are considerable differences in the performance of best management practices (BMP) depending on topographic, climatic, and land use conditions. The most important lesson is that no single BMP is sufficient and a wide range of practices need to be considered and integrated within a watershed context. The first step is to focus on rainwater management at the individual property level and then to scale up to the neighbourhood and watershed level. Using a water balance model allows a property owner to determine the amount of rainwater that needs to be collected, reused, detained, and infiltrated on site to prevent surface runoff. There are several on-site management options that can be used in combination, including roofwater collection and indoor/outdoor re-use, green roofs, rainfall interception by trees, minimizing impervious surfaces, developing rain gardens, and requiring at least 30 cm of topsoil on the property. At the neighbourhood and watershed scales the transportation networks should be linked to infiltration systems, swales, detention ponds and wetlands. The main emphasis is to minimize the use of conventional stormwater pipes and not to convey stormwater directly into streams. Infiltration systems have proven to be popular and effective in reducing stormwater peak flows but their effectiveness in detaining non-point source of pollution is still a major issue. This is a particular problem during the winter period and during snowmelt in central and eastern Canada where road salt and pollution from transportation systems cause significant challenges. There are considerable research needs to measure the effectiveness and long term performance of these detention systems in terms of pollution reduction under different climatic conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".